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Implementing Neuro Fuzzy Approach for Bus Arrival Time Prediction Using GPS Data
Fauzan M.a, Mores I.B.a, Nazaruddin Y.Y.b, Siregar P.I.a
a Instrumentation and Control Research Group, Institut Teknologi Bandung, Department of Engineering Physics, Bandung, Indonesia
b National Center for Sustainable Transportation Technology, CRCS, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.Bus arrival time estimator is very important in term of improving passengers’ convenience. Many stochastic factors must be considered to estimate arrival time such as traffic jam, bus velocity, passenger flow, etc. An alternative approach to predict bus arrival time by using Adaptive Neuro-Fuzzy Inference System (ANFIS) method will be assessed in this study which is aimed to observe its effectiveness and advantages as a universal approximator for highly non-linear functions. This study was conducted on some routes in the city of Bandung by inputting the real-time data such as range of time, week, and velocity of the bus. The proposed technique can estimate the arrival time with an expected result where the error criteria (RMSE value) is 0.618 in the learning stage and 0.878 in the validation stage.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Adaptive neuro-fuzzy inference system,Arrival time,Bus arrival time predictions,Neuro-fuzzy approach,Nonlinear functions,Stochastic factors,Transportation system,Universal approximators[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]adaptive neuro-fuzzy inference system,bus arrival time,transportation system[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][{‘$’: ‘This paper is supported by USAID through Sustainable Higher Education Research Alliances (SHERA) Program – Centre for Collaborative (CCR) National Center for Sustainable Transportation Technology (NCSTT).’}, {‘$’: ‘This paper is supported by USAID through Sustainable Higher Education Research Alliances (SHERA) Program – Centre for Collaborative (CCR) National Center for Sustainable Transportation Technology (NCSTT).’}][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICA.2019.8916669[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]